Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory90.3 B

Variable types

NUM11
BOOL1

Warnings

account has unique values Unique

Reproduction

Analysis started2021-05-17 07:07:53.367661
Analysis finished2021-05-17 07:08:13.029736
Duration19.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

account
Real number (ℝ≥0)

UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2068103284
Minimum37709441
Maximum4281711154
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:13.125758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum37709441
5-th percentile151250444.9
Q11212680590
median1994605610
Q32974652188
95-th percentile4059914832
Maximum4281711154
Range4244001713
Interquartile range (IQR)1761971598

Descriptive statistics

Standard deviation1209468316
Coefficient of variation (CV)0.5848200742
Kurtosis-0.987534946
Mean2068103284
Median Absolute Deviation (MAD)931986363
Skewness0.1087223221
Sum2.068103284e+11
Variance1.462813608e+18
MonotocityStrictly increasing
2021-05-17T17:08:13.267791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
267306905511.0%
 
21179248911.0%
 
134983457311.0%
 
374188091311.0%
 
425850272311.0%
 
387925870911.0%
 
427427285411.0%
 
290167228211.0%
 
406565257511.0%
 
3892387411.0%
 
Other values (90)9090.0%
 
ValueCountFrequency (%) 
3770944111.0%
 
3892387411.0%
 
5350854611.0%
 
8038849411.0%
 
9081474911.0%
 
ValueCountFrequency (%) 
428171115411.0%
 
427427285411.0%
 
425850272311.0%
 
416382218611.0%
 
406565257511.0%
 

age
Real number (ℝ≥0)

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.77
Minimum18
Maximum78
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:13.408823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile19
Q122
median29.5
Q339.25
95-th percentile50.1
Maximum78
Range60
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation11.54425389
Coefficient of variation (CV)0.3633696536
Kurtosis2.331432564
Mean31.77
Median Absolute Deviation (MAD)8.5
Skewness1.242431212
Sum3177
Variance133.269798
MonotocityNot monotonic
2021-05-17T17:08:13.541853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
1966.0%
 
2066.0%
 
2166.0%
 
3866.0%
 
3555.0%
 
4055.0%
 
2255.0%
 
2655.0%
 
2444.0%
 
2544.0%
 
Other values (23)4848.0%
 
ValueCountFrequency (%) 
1844.0%
 
1966.0%
 
2066.0%
 
2166.0%
 
2255.0%
 
ValueCountFrequency (%) 
7811.0%
 
6911.0%
 
6411.0%
 
5311.0%
 
5211.0%
 

X
Real number (ℝ)

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-38.6475
Minimum-573
Maximum-12.37
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:13.711891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-573
5-th percentile-37.931
Q1-37.76
median-33.975
Q3-31.895
95-th percentile-22.7555
Maximum-12.37
Range560.63
Interquartile range (IQR)5.865

Descriptive statistics

Standard deviation54.27292856
Coefficient of variation (CV)-1.404306321
Kurtosis97.75734874
Mean-38.6475
Median Absolute Deviation (MAD)3.7
Skewness-9.830716027
Sum-3864.75
Variance2945.550774
MonotocityNot monotonic
2021-05-17T17:08:13.848923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-37.8444.0%
 
-37.7633.0%
 
-37.6622.0%
 
-37.6922.0%
 
-33.7622.0%
 
-37.9122.0%
 
-37.8222.0%
 
-33.822.0%
 
-31.922.0%
 
-33.7722.0%
 
Other values (75)7777.0%
 
ValueCountFrequency (%) 
-57311.0%
 
-42.8811.0%
 
-38.0311.0%
 
-37.9711.0%
 
-37.9511.0%
 
ValueCountFrequency (%) 
-12.3711.0%
 
-12.4511.0%
 
-12.4911.0%
 
-17.0311.0%
 
-21.1511.0%
 

Y
Real number (ℝ≥0)

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.5658
Minimum114.62
Maximum255
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:13.996956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum114.62
5-th percentile115.79
Q1143.565
median145.155
Q3150.905
95-th percentile153.0905
Maximum255
Range140.38
Interquartile range (IQR)7.34

Descriptive statistics

Standard deviation16.19440982
Coefficient of variation (CV)0.1128013066
Kurtosis22.25234251
Mean143.5658
Median Absolute Deviation (MAD)5.745
Skewness2.682219338
Sum14356.58
Variance262.2589095
MonotocityNot monotonic
2021-05-17T17:08:14.136988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
144.9633.0%
 
151.2733.0%
 
150.922.0%
 
149.0322.0%
 
145.0322.0%
 
151.0422.0%
 
145.0422.0%
 
115.7922.0%
 
144.8922.0%
 
151.2322.0%
 
Other values (77)7878.0%
 
ValueCountFrequency (%) 
114.6211.0%
 
115.7211.0%
 
115.7411.0%
 
115.7811.0%
 
115.7922.0%
 
ValueCountFrequency (%) 
25511.0%
 
153.4122.0%
 
153.3211.0%
 
153.111.0%
 
153.0911.0%
 

gender_M
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size100.0 B
1
56 
0
44 
ValueCountFrequency (%) 
15656.0%
 
04444.0%
 
2021-05-17T17:08:14.248013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

balance
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137569.9247
Minimum13769.63
Maximum1584768.28
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:14.351036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13769.63
5-th percentile22724.108
Q149657.2425
median72025.43
Q3114188.5225
95-th percentile412686.295
Maximum1584768.28
Range1570998.65
Interquartile range (IQR)64531.28

Descriptive statistics

Standard deviation229368.2239
Coefficient of variation (CV)1.667284651
Kurtosis24.64846783
Mean137569.9247
Median Absolute Deviation (MAD)31707.625
Skewness4.64055257
Sum13756992.47
Variance5.260978212e+10
MonotocityNot monotonic
2021-05-17T17:08:14.503079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72025.4322.0%
 
83700.4211.0%
 
72949.8711.0%
 
65301.3311.0%
 
95447.7711.0%
 
51624.9511.0%
 
399484.9711.0%
 
76688.0511.0%
 
114575.0811.0%
 
36491.3811.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
13769.6311.0%
 
14129.5811.0%
 
16864.5911.0%
 
17476.4411.0%
 
20776.7611.0%
 
ValueCountFrequency (%) 
1584768.2811.0%
 
1398902.5511.0%
 
792776.2911.0%
 
506145.7211.0%
 
467645.2211.0%
 

annualized_sal
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60233.73235
Minimum25625.89097
Maximum126568.5443
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:14.673118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum25625.89097
5-th percentile32945.59736
Q141843.19386
median53080.91354
Q376392.92331
95-th percentile106372.7548
Maximum126568.5443
Range100942.6533
Interquartile range (IQR)34549.72945

Descriptive statistics

Standard deviation24004.8041
Coefficient of variation (CV)0.3985275884
Kurtosis0.00647635806
Mean60233.73235
Median Absolute Deviation (MAD)13380.29213
Skewness0.905904426
Sum6023373.235
Variance576230619.7
MonotocityNot monotonic
2021-05-17T17:08:14.817151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
53080.9135422.0%
 
126568.544311.0%
 
39884.6518611.0%
 
79003.9605111.0%
 
88177.3000811.0%
 
99704.5391611.0%
 
58410.8135211.0%
 
50728.3748411.0%
 
57752.0425311.0%
 
63638.0804511.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
25625.8909711.0%
 
26814.9446911.0%
 
30810.9730611.0%
 
30931.6904511.0%
 
31168.9353811.0%
 
ValueCountFrequency (%) 
126568.544311.0%
 
120481.501711.0%
 
118468.053211.0%
 
113741.919211.0%
 
110429.371111.0%
 

sales_pos
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1583.7283
Minimum19.24
Maximum5768.52
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:14.964176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19.24
5-th percentile217.3015
Q1781.7575
median1367.72
Q32069.325
95-th percentile3519.651
Maximum5768.52
Range5749.28
Interquartile range (IQR)1287.5675

Descriptive statistics

Standard deviation1159.669817
Coefficient of variation (CV)0.7322403828
Kurtosis1.776722889
Mean1583.7283
Median Absolute Deviation (MAD)655.44
Skewness1.23092758
Sum158372.83
Variance1344834.084
MonotocityNot monotonic
2021-05-17T17:08:15.106216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1367.7222.0%
 
1876.1311.0%
 
1229.8611.0%
 
1534.6711.0%
 
410.0411.0%
 
1296.6611.0%
 
2835.8211.0%
 
1940.8811.0%
 
546.911.0%
 
2114.111.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
19.2411.0%
 
91.8511.0%
 
104.5511.0%
 
170.7211.0%
 
192.6311.0%
 
ValueCountFrequency (%) 
5768.5211.0%
 
5020.7111.0%
 
4988.2411.0%
 
4556.7511.0%
 
3622.2711.0%
 

pos
Real number (ℝ≥0)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1540.1578
Minimum19.76
Maximum8244.07
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:15.266252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum19.76
5-th percentile220.0785
Q1672.4175
median1225.75
Q31808.9425
95-th percentile3671.602
Maximum8244.07
Range8224.31
Interquartile range (IQR)1136.525

Descriptive statistics

Standard deviation1318.968739
Coefficient of variation (CV)0.8563854558
Kurtosis8.374279997
Mean1540.1578
Median Absolute Deviation (MAD)566.91
Skewness2.445237173
Sum154015.78
Variance1739678.536
MonotocityNot monotonic
2021-05-17T17:08:15.403275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1225.7522.0%
 
675.5411.0%
 
574.4211.0%
 
969.8311.0%
 
786.9111.0%
 
1369.511.0%
 
1700.8811.0%
 
343.8411.0%
 
1306.7211.0%
 
2499.7611.0%
 
Other values (89)8989.0%
 
ValueCountFrequency (%) 
19.7611.0%
 
31.2611.0%
 
122.8611.0%
 
160.6811.0%
 
209.611.0%
 
ValueCountFrequency (%) 
8244.0711.0%
 
6636.1311.0%
 
5615.7911.0%
 
4830.9711.0%
 
3743.4611.0%
 

payment
Real number (ℝ≥0)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2026.74
Minimum338
Maximum5332
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:15.544307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum338
5-th percentile575.25
Q11234.5
median1815
Q32611
95-th percentile4138.4
Maximum5332
Range4994
Interquartile range (IQR)1376.5

Descriptive statistics

Standard deviation1080.672449
Coefficient of variation (CV)0.5332072436
Kurtosis0.3821120729
Mean2026.74
Median Absolute Deviation (MAD)633.5
Skewness0.8484299859
Sum202674
Variance1167852.942
MonotocityNot monotonic
2021-05-17T17:08:15.689340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
48622.0%
 
173422.0%
 
181522.0%
 
166022.0%
 
244211.0%
 
326711.0%
 
184611.0%
 
99511.0%
 
88411.0%
 
187811.0%
 
Other values (86)8686.0%
 
ValueCountFrequency (%) 
33811.0%
 
39011.0%
 
48622.0%
 
50411.0%
 
57911.0%
 
ValueCountFrequency (%) 
533211.0%
 
467111.0%
 
458111.0%
 
447211.0%
 
429811.0%
 

inter_bank
Real number (ℝ≥0)

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878.35
Minimum213
Maximum3794
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:15.832372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum213
5-th percentile263.8
Q1570.75
median734.5
Q3903.75
95-th percentile1965.6
Maximum3794
Range3581
Interquartile range (IQR)333

Descriptive statistics

Standard deviation611.1185204
Coefficient of variation (CV)0.6957574092
Kurtosis9.1552835
Mean878.35
Median Absolute Deviation (MAD)171
Skewness2.686408866
Sum87835
Variance373465.846
MonotocityNot monotonic
2021-05-17T17:08:15.971412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
734.53232.0%
 
70222.0%
 
77122.0%
 
116411.0%
 
54611.0%
 
42911.0%
 
53611.0%
 
83711.0%
 
21311.0%
 
23411.0%
 
Other values (57)5757.0%
 
ValueCountFrequency (%) 
21311.0%
 
22811.0%
 
23411.0%
 
25011.0%
 
26011.0%
 
ValueCountFrequency (%) 
379411.0%
 
367311.0%
 
283311.0%
 
216011.0%
 
214811.0%
 

phone_bank
Real number (ℝ≥0)

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541.86
Minimum246
Maximum1916
Zeros0
Zeros (%)0.0%
Memory size800.0 B
2021-05-17T17:08:16.109435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum246
5-th percentile472.8
Q1517.5
median517.5
Q3517.5
95-th percentile565.85
Maximum1916
Range1670
Interquartile range (IQR)0

Descriptive statistics

Standard deviation189.5919339
Coefficient of variation (CV)0.3498909939
Kurtosis38.39770416
Mean541.86
Median Absolute Deviation (MAD)0
Skewness5.857341774
Sum54186
Variance35945.10141
MonotocityNot monotonic
2021-05-17T17:08:16.212467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
517.58484.0%
 
162911.0%
 
79311.0%
 
191611.0%
 
55911.0%
 
53111.0%
 
45011.0%
 
69611.0%
 
35511.0%
 
40211.0%
 
Other values (7)77.0%
 
ValueCountFrequency (%) 
24611.0%
 
25211.0%
 
35511.0%
 
40211.0%
 
45011.0%
 
ValueCountFrequency (%) 
191611.0%
 
162911.0%
 
87111.0%
 
79311.0%
 
69611.0%
 

Interactions

2021-05-17T17:07:56.831965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:56.956993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.077020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.196047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.321084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.453106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.566131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.686167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.797192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:57.909209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.032246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.154274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.275302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.400330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.528359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.660389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.802430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:58.923458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.050486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.167521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.286540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.415569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.543598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.663634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.789654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:07:59.915691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.207749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.346780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.470808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.599838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.721875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.840902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:00.968931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-17T17:08:01.904143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.038173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.161201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.285238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.419269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.554290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.691322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.834382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:02.977414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.125448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.280483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.417524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.561557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.695596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.831618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:03.976651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.124685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.241711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.363739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.481766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.603793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.736823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.850850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:04.973877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.087903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.200928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.321956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.441983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.564011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.693040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.821069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:05.954100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.096132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.217171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.345208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.474230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.595257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.728287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.858316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:06.970342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.086368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.202394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.322431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.450469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.561486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.679512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.785537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:07.893561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:08.009587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-17T17:08:08.470700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:08.792765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:08.921794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-05-17T17:08:10.267099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:10.388135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:10.525158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:10.650186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:10.772214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:10.901243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.031273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.156301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.290331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.426362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.563394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.709426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.835455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:11.968485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:12.089513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:12.217542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:12.351573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-17T17:08:16.606548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-17T17:08:16.848611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-17T17:08:17.091669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-17T17:08:17.337733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-17T17:08:12.596628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-17T17:08:12.904698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

accountageXYgender_Mbalanceannualized_salsales_pospospaymentinter_bankphone_bank
03770944118-28.01153.41198107.03103584.219036399.84663.052088.01692.0252.0
13892387438-33.90151.271506145.7287565.226052734.411716.744671.0827.0517.5
25350854635-33.76150.62054704.1131168.9353831204.22350.311180.0734.5517.5
38038849428-37.42144.97135050.3241178.7737493033.071356.47852.0270.0517.5
49081474935-32.98151.68165301.3349604.8544451472.621845.16831.0902.01916.0
515443127118-27.48153.09017476.4433039.1058851229.862390.651734.0883.0517.5
618244657427-32.00116.06050023.1071195.1463733048.653483.64945.01716.0517.5
721179248930-34.93138.63014129.5835172.4123551763.542014.291801.0734.5517.5
824080474329-30.75121.481106299.10110429.3710951876.13766.902747.0734.5559.0
935410665839-33.80151.04078807.9491379.7665282567.761597.611635.0350.0531.0

Last rows

accountageXYgender_Mbalanceannualized_salsales_pospospaymentinter_bankphone_bank
90387925870940-37.66143.8301398902.55105685.1169531527.511422.822672.0917.0492.0
91388103119037-21.15149.19172025.4339395.193174410.041426.07996.0702.0517.5
92394118108725-31.94115.79154667.7157752.042526297.19160.682000.0576.0517.5
93395467788747-32.28115.72075920.3259238.216430871.691023.704094.0702.0517.5
94405961284538-12.49130.981399484.9766362.905693242.31409.651734.0585.0517.5
95406565257521-31.82115.81183700.4289707.4135403369.45606.373957.01001.0517.5
96416382218626-34.97149.03094134.8739565.364938104.5519.762442.0734.5517.5
97425850272324-37.74145.45051624.9549339.4982212662.223667.821984.0670.0517.5
98427427285420-37.86145.23142000.3035255.421225233.121369.501183.0734.5517.5
99428171115442-37.84144.981275038.6644063.9586632054.40122.861438.0734.5504.0